A Multicenter Matched Comparison of Transanal and Robotic Total Mesorectal Excision for Mid and Low-rectal Adenocarcinoma
Bibliographic record
Abstract
OBJECTIVE: To compare the quality of surgical resection of transanal total mesorectal excision (TA-TME) and robotic total mesorectal excision (R-TME). BACKGROUND: Both TA-TME and R-TME have been advocated to improve the quality of surgery for rectal cancer below 10 cm from the anal verge, but there are little data comparing TA-TME and R-TME. METHODS: Data of patients undergoing TA-TME or R-TME for rectal cancer below 10 cm from the anal verge and a sphincter-saving procedure from 5 high-volume rectal cancer referral centers between 2011 and 2017 were obtained. Coarsened exact matching was used to create balanced cohorts of TA-TME and R-TME. The main outcome was the incidence of poor-quality surgical resection, defined as a composite measure including incomplete quality of TME, or positive circumferential resection margin (CRM) or distal resection margin (DRM). RESULTS: Out of a total of 730 patients (277 TA-TME, 453 R-TME), matched groups of 226 TA-TME and 370 R-TME patients were created. These groups were well-balanced. The mean tumor height from the anal verge was 5.6 cm (SD 2.5), and 70% received preoperative radiotherapy. The incidence of poor-quality resection was similar in both groups (TA-TME 6.9% vs R-TME 6.8%; P = 0.954). There were no differences in TME specimen quality (complete or near-complete TA-TME 99.1% vs R-TME 99.2%; P = 0.923) and CRM (5.6% vs 6.0%; P = 0.839). DRM involvement may be higher after TA-TME (1.8% vs 0.3%; P = 0.051). CONCLUSIONS: High-quality TME for patients with rectal adenocarcinoma of the mid and low rectum can be equally achieved by transanal or robotic approaches in skilled hands, but attention should be paid to the distal margin.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".